Forecasting Store Foot Traffic Using Facial Recognition, Time Series and Support Vector Machines

  • Paulo CortezEmail author
  • Luís Miguel Matos
  • Pedro José Pereira
  • Nuno Santos
  • Duarte Duque
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 527)


In this paper, we explore data collected in a pilot project that used a digital camera and facial recognition to detect foot traffic to a sports store. Using a time series approach, we model daily incoming store traffic under three classes (all faces, female, male) and compare six forecasting approaches, including Holt-Winters (HW), a Support Vector Machine (SVM) and a HW-SVM hybrid that includes other data features (e.g., weather conditions). Several experiments were held, under a robust rolling windows scheme that considers up to one week ahead predictions and two metrics (predictive error and estimated store benefit). Overall, competitive results were achieved by the SVM (all faces), HW (female) and HW-SVM (male) methods, which can potentially lead to valuable gains (e.g., enhancing store marketing or human resource management).


Data mining Facial recognition Time series forecasting Support vector machine 



This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Paulo Cortez
    • 1
    Email author
  • Luís Miguel Matos
    • 1
  • Pedro José Pereira
    • 1
  • Nuno Santos
    • 2
  • Duarte Duque
    • 3
  1. 1.Department of Information Systems, ALGORITMI CentreUniversity of MinhoGuimarãesPortugal
  2. 2.EXVA TechnologiesBragaPortugal
  3. 3.ALGORITMI CentreDIGARC - Polytechnic Institute of Cavado and AveBarcelosPortugal

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